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FOIA is great…if you know who and what to ask for

Dooring is dangerous (sometimes deadly) for bicyclists. Where's the data? Image via The Blaze

Dooring is dangerous (sometimes deadly) for bicyclists. Where’s the data? Image via The Blaze

tl;dr: This is the list of all citation types that the Chicago Dept. of Administrative Hearings “administers”.

The Freedom of Information Act is my favorite law because it gives the public – and me – great access to work, information, and data that the public – including me – causes to have created for the purpose of running governments.

FOIA requires public agencies to publish (really, email you) stuff that they make and don’t publish on their own (which is dumb), and reply to you within five days.

All you have to do is ask for it!

BUT: Who do you ask?

AND: What do you ask them for?

This is the hardest thing about submitting a FOIA request.

Lately, my friend and I – more my friend than me – have been trying to obtain data on the number of traffic citations issued to motorists for opening their door into traffic – a.k.a. “dooring”.

It is dangerous everywhere, and in Chicago this is illegal. In Chicago it carries a steep fine. $500 if you don’t hurt a bicyclist, and $1,000 if you do.

My friend FOIA’d the Chicago Police Department. You know, the agency that actually writes the citations. They don’t have bulk records to provide.

Then he FOIA’d the Chicago Department of Transportation, the Illinois Department of Transportation, the Chicago Department of Administrative Hearings, and the Chicago Department of Finance.

Each of these five agencies tells you on their website how to submit a FOIA request. You can also use FOIA Machine to help you find a destination for your request.

None of them have the records either. The “FOIA officer” for the Administrative Hearings department suggested that he contact the Cook County Circuit Court. So that’s what we’re doing.

Oh, and since the Administrative Hearings department doesn’t have this information (even though they have the records of citations for a lot of other traffic violations), I figured I would ask for them for a list of citations that they do have records of.

And here’s the list, all 3,857 citation types. You’ll notice a lot of them don’t have a description, and some of very short and unclear descriptions. Hopefully you can help me fix that!

I can grant you editing access on the Google Doc and we can improve this list with some categorizations, like “building violations” and “vehicle code”.

 

Working with ZIP code data (and alternatives to using sketchy ZIP code data)

1711 North Kimball Avenue, built 1890

This building at 1711 N Kimball no longer receives mail and the local mail carrier would mark it as vacant. After a minimum length of time the address will appear in the United States Postal Service’s vacancy dataset, provided by the federal Department of Housing and Urban Development. Photo: Gabriel X. Michael.

Working with accurate ZIP code data in your geographic publication (website or report) or demographic analysis can be problematic. The most accurate dataset – perhaps the only one that could be called reliably accurate – is one that you purchase from one of the United States Postal Service’s (USPS) authorized resellers. If you want to skip the introduction on what ZIP codes really represent, jump to “ZIP-code related datasets”.

Understanding what ZIP codes are

In other words the post office’s ZIP code data, which they use to deliver mail and not to locate people like your publication or analysis, is not free. It is also, unbeknownst to many, a dataset that lists mail carrier routes. It’s not a boundary or polygon, although many of the authorized resellers transform it into a boundary so buyers can geocode the location of their customers (retail companies might use this for customer tracking and profiling, and petition-creating websites for determining your elected officials).

The Census Bureau has its own issues using ZIP code data. For one, the ZIP code data changes as routes change and as delivery points change. Census boundaries needs to stay somewhat constant to be able to compare geographies over time, and Census tracts stay the same for a period of 10 years (between the decennial surveys).

Understanding that ZIP codes are well known (everybody has one and everybody knows theirs) and that it would be useful to present data on that level, the Bureau created “ZIP Code Tabulation Areas” (ZCTA) for the 2000 Census. They’re a collection of Census tracts that resemble a ZIP code’s area (they also often share the same 5-digit identifiers). The ZCTA and an area representing a ZIP code have a lot of overlap and can share much of the same space. ZCTA data is freely downloadable from the Census Bureau’s TIGER shapefiles website.

There’s a good discussion about what ZIP codes are and aren’t on the GIS StackExchange.

Chicago example of the problem

Here’s a real world example of the kinds of problems that ZIP code data availability and comprehension: Those working on the Chicago Health Atlas have run into this problem where they were using two different datasets: ZCTA from the Census Bureau and ZIP codes as prepared by the City of Chicago and published on their open data portal. Their solution, which is really a stopgap measure and needs further review not just by those involved in the app but by a diverse group of data experts, was to add a disclaimer that they use ZCTAs instead of the USPS’s ZIP code data.

ZIP-code related datasets

Fast forward to why I’m telling you all of this: The U.S. Department of Housing and Urban Development (HUD) has two ZIP-code based datasets that may prove useful to mappers and researchers.

1. ZIP code crosswalk files

This is a collection of eight datasets that link a level of Census geography to ZIP codes (and the reverse). The most useful to me is ZIP to Census tract. This dataset tells you in which ZIP code a Census tract lies (including if it spans multiple ZIP codes). HUD is using data from the USPS to create this.

The dataset is documented well on their website and updated quarterly, going back to 2010. The most recent file comes as a 12 MB Excel spreadsheet.

2. Vacant addresses

The USPS employs thousands of mail carriers to delivery things to the millions of households across the country, and they keep track of when the mail carrier cannot delivery something because no one lives in the apartment or house anymore. The address vacancy data tells you the following characteristics at the Census tract level:

  • total number of addresses the USPS knows about
  • number of addresses on urban routes to which the mail carrier hasn’t been able to delivery for 90 days and longer
  • “no-stat” addresses: undeliverable rural addresses, places under construction, urban addresses unlikely to be active

You must register to download the vacant addresses data and be a governmental entity or non-profit organization*, per the agreement** HUD has with USPS. Learn more and download the vacancy data which they update quarterly.

Tina Fassett Smith is a researcher at DePaul University’s Institute of Housing Studies and reviewed part of this blog post. She stresses to readers to ignore the “no-stat” addresses in the USPS’s vacancy dataset. She said that research by her and her colleagues at the IHS concluded this section of the data is unreliable. Tina also said that the methodology mail carriers use to identify vacant addresses and places under change (construction or demolition) isn’t made public and that mail carriers have an incentive to collect the data instead of being compensated normally. Tina further explained the issues with no-stat.

We have seen instances of a relationship between the number of P.O. boxes (i.e., the presence of a post office) and the number of no-stats in an area. This is one reason we took it off of the IHS Data Portal. We have not found it to be a useful data set for better understanding neighborhoods or housing markets.

The Institute of Housing Studies provides vacancy data on their portal for those who don’t want to bother with the HUD sign-up process to obtain it.

* It appears that HUD doesn’t verify your eligibility.

** This agreement also states that one can only use the vacancy data for the “stated purpose”: “measuring and forecasting neighborhood changes, assessing neighborhood needs, and measuring/assessing the various HUD programs in which Users are involved”.

Who bikes?

Who bikes? pie chart

From April 2011, via Sightlines Daily, using data from John Pucher and Ralph Buehler, who got it from the 2009 National Household Travel Survey.

Contrary to popular convention, the biggest share of bicyclists isn’t yuppies, it’s low income people. In fact, the lowest-earning quarter of Americans make nearly one-third of all bike trips. Among that group, I would expect to find at least some fraction of working poor, students, the unemployed, and retired people of modest means. No doubt there are almost as many reasons to bike as there are cyclists, but it’s clear that bikes are a favored choice among those on a budget.

The big takeaway for me, however, is looking beyond low-income riders. Bicycling is remarkably evenly distributed among the remaining three quartiles. With the exception of the over- represented bottom quartile, bike trips don’t appear to be the province of any one income class more than any other.

Introduction to DIY bike ridership research

A lot of people ask me how many people are out there bicycling.

“Not a lot”, I tell them.

And I explain why: the primary source of data is the American Community Survey, which is a questionnaire that asks people questions about how they got to work in a specific week. (More details on how it does this below.) We don’t have data, except in rare “Household Travel Surveys”, about trips by bike to school, shopping, and social activities.

It’s comparable across the country – you can get this data for any city.

Here’s how:

  1. Visit the “legacy” American FactFinder and select American Community Survey, operated by the United States Census Bureau.
  2. Select 2005-2009 American Community Survey 5-Year Estimates (or the latest 5-year estimate). This is the most accurate data.
  3. In the right-side menu that appears, click on “Enter a table number”.
  4. In the new window, input the table number ” S0801″ (“Commuting Characteristics by Sex”) and submit the form. The new window will close and the other window will go to that table.
  5. Now it’s time to select your geography. In the left-side menu, under “Change…” click on “geography (state, county, place…)”
  6. In the window to change your geography, select “Place” as your “Geographic Type”.
  7. Then select the state.
  8. Then select your city and click “Show Result”.
Notes:
  • This data shows all modes people take to work, who live in that city. It’s highly probable that people are leaving the city to their jobs on these modes. For example, someone who lives in Rogers Park may ride their bike to work in Evanston.
  • The URL is a permanent link to this dataset. Each city has a unique URL. You should save these as bookmarks so you can easily reference the data later.
  • The question on the survey doesn’t allow multiple choices: “People who used more than one means of transportation to get to work each day were asked to report the one used for the longest distance during the work trip”.

Policy insight for Monday, August 1, 2011

This isn’t refined. These are just my notes that I speak from. I may not have spoke about everything written here and I may not have written here everything I spoke about. This is for Moving Design

There was report of cyclist crashing on the Tuff Curb at the on-street bike parking facility in Wicker Park.

Installing the Tuff Curb

experimental projects need reviews. I don’t mean projects that are considered experiments, I mean projects that are new to the people who designed it, and new to the people who will be using it.

we need good data collection.

Did the Kinzie bike lane cause congestion? So what if it did?
We would need data points that were collected using well-known methods, and probably at different times of the day and week. And we’d have to be sure to count cyclists, too.
Then 3, 6, or 12 months later, we’d have to do it again.

What was the change?
Is that a change that meets our goals?

Back to the cyclist crashing on tuff curb, what is the city’s plan to monitor the use (or disuse) of the facility? How will the city collect data on something like this?

Census – not gonna happen in 2020
American Community Survey – 5-year estimates (with data gathered annually) will replace decennial Census.

“Here are a few Streetsblog posts about Census and NYC DOT’s bike counts, and the problems with each. The first post has some stuff about what could be done to improve on them:” (Ben Fried, Editor in Chief, Streetsblog NYC)

http://www.streetsblog.org/2010/04/27/how-many-new-yorkers-bike-each-day/
http://www.streetsblog.org/2010/10/01/did-nyc-bike-commuting-decrease-in-2009-thats-what-the-census-says/
http://www.streetsblog.org/2011/04/13/actually-if-you-build-it-they-will-bike/

Read more policy insights from Steven Vance. 

Collecting the wrong information doesn’t help us plan well

The Illinois Traffic Crash Report (see scan below) has a field in the upper left titled “PEDV” which means “Pedalcyclist or pedestrian visibility.”

The possible entries for this field are the following codes*:

  1. No contrasting clothing
  2. Contrasting clothing
  3. Reflective material
  4. Other light source used

For my crash report, the police officer noted “1 – No contrasting clothing.” I don’t remember what I was wearing that night, so I can’t dispute that. I didn’t have lighting required by state law. I don’t know if the police officer would mark “4 – Other light source used” if I did. I’m not aware of what kind of guidance the report or data dictionary offers the police officer filling out the report; how is “contrasting clothing” defined?

Wearing contrasting clothing is not required by law. Using a headlight while bicycling at “nighttime” is. The light will be more effective than any kind of clothing in increasing the visibility of the bicyclist.

The crash report should note the bicyclist’s compliance with state law, not whether or not their clothing choice may have been a contributing factor in the crash (which the presence of this code on the report implies). I took the photo below last night when I was wearing a black jacket and gray jeans. It doesn’t appear very contrasting – but I was in compliant with state and city laws about lighting at night.

My clothes may blend into the night, by my blinking light surely doesn’t.

Collecting information on lighting law compliance could help cities and police better plan education and enforcement initiatives. It can give us information on crashes that we wouldn’t otherwise have, like how many crashes involved cyclists who didn’t have the required lights. Or where a lot of crashes occur even though a high percentage of cyclists involved there had sufficient lighting.

Illinois cyclists had a big win with the inclusion of doorings in state-provided crash reports. I think the next change should be to record information on compliance with lighting laws. If you need a good light, try this one from Planet Bike.

*This information comes from the “2004-present person codes” data dictionary from the Illinois Department of Transportation.

New site brings together bike crash maps and projects

I just finished creating a website that brings together my original Chicago bike crash map and all of its offshoots created by others. It also includes a more details and updated FAQ page as well as a short history of how the map and data came to be.

Enter the Crash Portal.

Right now it features projects from myself, Francesco Villa, Derek Eder, and George Aye’s students at the School of the Art Institute “Living in a Smart City” class. The site also links to my inspiration: Boston and San Francisco. If you have a related project, email me and I’ll figure out a way to add it to the site.

Screenshot of new Crash Portal

My television interview about dooring data

Last week you heard me on WGN 720 AM talk about bicycling in Chicago and my bike crash map.

This week you’ll get to see me talk about bike crash and dooring data on WTTW’s Chicago Tonight program. It comes after a rule change announced on Sunday: the Illinois Department of Transportation will begin collecting crash reports for doorings. Previously, these were “unreportable.”

WTTW reporter Ash-har Quraishi came over to my house Thursday to ask me about what kind of information the crash data I obtained from IDOT includes and excludes.

Initial intersection crash analysis for Milwaukee Avenue

Slightly upgraded Chicago Crash Browser

This screenshot from the Chicago Crash Browser map shows the location of bike-car collisions at Ogden/Milwaukee, an intersection that exemplifies the yellow trap problem the city hasn’t remedied.

List of the most crash-prone intersections on Milwaukee Avenue in Chicago. Using data from 2007-2009, when reported to the Chicago Police Department. Dooring data not included on the bike crash map. I used QGIS to draw a 50-feet buffer around the point where the intersection center lines meet.

Intersecting street (class 4*) Bike crashes
Chicago Avenue (see Ogden below) 12 (17)
California Avenue 9
Halsted Street & Grand Avenue 7
Damen Avenue & North Avenue 6
Western Avenue 6
Ogden Avenue (see Chicago above) 5 (17)
Ashland Avenue 5
Diversey Avenue 5
Fullerton Avenue 5
Elston Avenue 5
Augusta Boulevard (not class 4) 5

Combine the six-way (with center triangle) intersection of Ogden, Milwaukee, Chicago, and you see 17 crashes. Add the 6 just outside the 50-feet buffer and you get 23 crashes. Compare this to the six-way (without center triangle) at Halsted, Milwaukee, Grand, where there’s only 7 crashes.

What about the two intersections causes such a difference in crashes? Let’s look at some data:

Ogden, Milwaukee, Chicago Halsted, Milwaukee, Grand
Automobile traffic Approx 58,000 cars per day Approx 50,000 cars per day.
Bicycle traffic Not counted, but probably fewer than 3,100 bikes More than 3,100 bikes per day*
Bus traffic Two bus routes Three bus routes
Intersection style Island; three signal cycles No island; one signal cycle

*Notes

Traffic counts are assumed estimates. Counts are taken on a single day, either Tuesday, Wednesday, or Thursday. Bike counts at Halsted/Milwaukee/Grand were actually taken on Milwaukee several hundred feet northwest of the intersection so DO NOT include people biking on Halsted or Grand! This means that more than 3,100 people are biking through the intersection each day.

Intersection style tells us which kind of six-way intersection it is. At island styles you’ll find a concrete traffic island separating the three streets. You’ll also find three signal cycles because there are actually three intersections instead of one, making it a 12-way intersection. Also at these intersections you’ll see confusing instructional signage like, “OBEY YOUR SIGNAL ONLY” and “ONCOMING TRAFFIC HAS LONGER GREEN.”

These intersections are more likely to have a “yellow trap” – Ogden/Milwaukee definitely has this problem. The yellow trap occurs at that intersections when northbound, left-turning motorists (from Milwaukee to Ogden) get a red light but they still need to vacate the intersection. Thinking that oncoming traffic has a red light but are just being jerks and blowing the red light (when in fact they still have a green for 5-10 more seconds) they turn and sometimes hit the southbound traffic. The City of Chicago acknowledged this problem, for bicyclists especially, in summer 2013 but as of November 2014 the issue remains.

Here’s a more lengthy description of one of the problems here as well as an extremely simple solution: install a left-turn arrow for northbound Milwaukee Avenue. The entire intersection is within Alderman Burnett’s Ward 27.

Source and method

I can’t yet tell you how I obtained this data or created the map. I’m still working out the specifics in my procedures log. It involved some manual work at the end because in the resulting table that counted the number of crashes per intersection, every intersection was repeated, but the street names were in opposite columns.

Crash data from the Illinois Department of Transportation. Street data from the City of Chicago. Intersection data created with fTools in QGIS. To save time in this initial analysis, I only considered Milwaukee Avenue intersections with streets in the City of Chicago centerline file with a labeled CLASS of 1, 2, or 3.

Illinois will finally begin tracking dooring bike crashes

Governor Quinn made a rule change today requiring Illinois police departments to record dooring-type bicycle crashes on the SR-1050 motorist crash reporting form, according to Jon Hilkevitch of the Chicago Tribune. The announcement will be made tomorrow.

Apparently, Gov. Quinn read the Chicago Tribune’s article on March 21st about how the Illinois Department of Transportation could not and would not collect information on dooring crashes. I first wrote about this data deficiency on March 11.

For now, responding police officers will have to write DOORING next to the bicyclist’s name on the crash reporting form (the Chicago Police method was to write DOORING on a second piece of paper and record this data internally – IDOT would not accept the second page). The Tribune article explains that IDOT already ordered a bunch of new forms and won’t make a new order until 2013 at which time the form will have a checkbox making this process much simpler.

I would like to thank Governor Quinn, writer Jon Hilkevitch, Amanda Woodall, the Active Transportation Alliance, and all who contacted IDOT asking for their reporting standards to be changed to record dooring crashes. This means that next year you’ll see bike crash maps with a ton more dots – those of doorings, unless we continue educating ourselves, family and friends about riding AWAY from the door zone.

Why collecting this data is important

From the article:

[Active Transportation] Alliance officials said dooring accidents are common, basing the conclusion on reports from bicyclists. But without a standardized statewide reporting system, there has been no way to accurately quantify the problem or pinpoint locations where such accidents frequently occur and where modifications to street layouts would help, alliance officials said.

“We hope to use the data to obtain funding for education safety so drivers as well as bicyclists know what the risks are and what the factors are to create safer roadways,” said Dan Persky, director of education at the alliance.

Ride out of the door zone. Illustration by Gary Kavanagh.

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